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Facilitating Team Based Learning – tools to succeed: pre-class preparation.
Traditionally, the pre-class preparation phase of TBL involved students covering readings prior to a session (Michaelsen, 2002). Pre-class readings may have included chapters/excerpts from textbooks and articles or written content created by the educator. Nowadays, we can share a variety of different types of content with students (Parmelee et al., 2012). We are not confined to “text”. We can also provide access to content electronically e.g. through learning management systems (LMSs). This helps to cut down on paper usage. Here we outline 5 technology-related tips to keep in mind when we are considering how we will design the pre-class preparation phase. Click on the links throughout the document for more information and guidance.
Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)
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This is an interactive multimedia open educational resource (OER), created using H5P, on the topic of Open Education. It has been developed by the Department of Technology Enhanced Learning at Munster Technological University.
The purpose of which is to give an overview of OEPs, using interactive features of H5P
The purpose of this resource is to allow Moodle teachers to quickly create quizzes for Moodle. Users can quickly select between questions types through a single interface and export their questions as an XML file which can be imported into Moodle.
An open educational resource that supports academic, professional, and support staff to build more neuro-inclusive teaching, services, and policies in higher education. This resource was conceptualised & developed through the experiences of neurodivergent students in third-level education, with co-creation at the heart of every stage of the process
This OER presents an updated Assessment Redesign Framework for higher education in the age of GenAI. It supports educators and programme teams in designing valid, transparent, and scalable assessments, integrating AI literacy, process-focused approaches, and guidance on AI detection, large cohorts, and emerging agentic AI challenges.
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